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Deep Convective Transport to the Upper-Troposphere/Lower-Stratosphere

Deep Convective Transport to the Upper-Troposphere/Lower-Stratosphere
到对流层上层/平流层下层的深对流输送
批准号:
0918010
负责人:
Gretchen Mullendore
金额:
$33.32万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-15 至 2013-07-31

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中文摘要
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英文摘要
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The gases in the upper troposphere and lower stratosphere (UT/LS) have a significant effect on the chemical and radiative budgets of the global atmosphere, crucial to understanding climate change. To understand the chemical makeup of the UT/LS region, a much better understanding of deep convective transport is needed. Transport in deep convection is unique in its ability to entrain constituents from the near surface and rapidly transport them to the UT/LS region where they are detrained. The variability of entrainment and detrainment in storms is not captured in current climate model convective transport parameterizations.This study proposes to address these problems with a tripartite approach. First, using dual-Doppler velocity data from field campaigns, the observed detrainment profile will be derived for a suite of storms covering a range of storm morphologies and background environments. Second, radar reflectivity data from the same field campaigns will be used to look for relationships between velocity-derived detrainment profiles and the reflectivity fields. The goal of the second part of the proposal is to define an algorithm for processing radar reflectivity observations that will allow for estimates of convective mass transport based on radar reflectivity observations over any region covered by the weather radar network. Third, cloud-resolving models will be used to assess the quality of reflectivity-based transport estimates for specific storms. Using the combination of radar reflectivity observations and modeling, a climatology of convective transport over the central United States will be compiled.Improvements in understanding and modeling of deep convective transport resulting from this study will directly benefit many complementary modeling efforts and improve understanding of convective transport of gases into the UT/LS. Ultimately, these results can be used to reduce uncertainty in climate prediction models, a critical need considering the climate challenges facing humanity.
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Collaborative Research: EarthCube RCN: "What About Model Data?": Determining Best Practices for Archiving and Reproducibility
Collaborative Research: SI2-SSI: Big Weather Web: A Common and Sustainable Big Data Infrastructure in Support of Weather Prediction Research and Education in Universities
Midlatitude Deep Convective Transport to the Upper-Troposphere and Lower-Stratosphere
EAGER: Educational Contributions to the Deep Convective Clouds and Chemistry (DC3) Field Campaign
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